Medical monitoring device for harmonizing physiological measurements

ABSTRACT

Systems, methods, apparatuses, and medical devices for harmonizing data from a plurality of non-invasive sensors are described. A physiological parameter can be determined by harmonizing data between two or more different types of non-invasive physiological sensors interrogating the same or proximate measurement sites. Data from one or more first non-invasive sensors can be utilized to identify one or more variables that are useful in one or more calculations associated with data from one or more second non-invasive sensors. Data from one or more first non-invasive sensors can be utilized to calibrate one or more second non-invasive sensors. Non-invasive sensors can include, but are not limited to, an optical coherence tomography (OCT) sensor, a bio-impedance sensor, a tissue dielectric constant sensor, a plethysmograph sensor, or a Raman spectrometer.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority benefit to U.S. ProvisionalApplication No. 62/532,273, entitled “MEDICAL MONITORING DEVICE FORCOORDINATING PHYSIOLOGICAL MEASUREMENTS,” filed Jul. 13, 2017, and U.S.Provisional Application No. 62/667,983, entitled “MEDICAL MONITORINGDEVICE FOR HARMONIZING PHYSIOLOGICAL MEASUREMENTS,” filed May 7, 2018,each of which is hereby incorporated herein by reference in itsentirety.

TECHNICAL FIELD

The present disclosure relates to blood glucose determination inbiological tissues. Specifically, this disclosure relates to systems,methods, and apparatuses for harmonizing data from a plurality ofnon-invasive sensors to estimate blood glucose levels.

BACKGROUND

Monitoring of blood glucose (blood sugar) concentration levels has longbeen critical to the treatment of diabetes in humans. Current bloodglucose monitors involve a chemical reaction between blood serum and atest strip, requiring an invasive extraction of blood via a lancet orpinprick. Small handheld monitors have been developed to enable apatient to perform this procedure anywhere, at any time. But theinconvenience of this procedure—specifically the blood extraction andthe use and disposition of test strips—has led to a low level ofcompliance. Such low compliance can lead to serious medicalcomplications. Thus, a non-invasive method for monitoring blood glucoseis needed.

SUMMARY

The present disclosure describes example systems, methods, apparatuses,and medical devices for harmonizing data from a plurality ofnon-invasive sensors. In general, a physiological parameter can bedetermined by harmonizing data between two or more different types ofnon-invasive physiological sensors interrogating the same or proximatemeasurement sites. In some cases, data from one or more firstnon-invasive sensors can be utilized to identify one or more variablesthat are useful in one or more calculations associated with data fromone or more second non-invasive sensors. In some cases, data from one ormore first non-invasive sensors can be utilized to calibrate one or moresecond non-invasive sensors. Non-invasive sensors can include, but arenot limited to, an optical coherence tomography (OCT) sensor, abio-impedance sensor, a tissue dielectric constant sensor, aplethysmography sensor, or a Raman spectrometer.

For purposes of summarizing the disclosure, certain aspects, advantagesand novel features are discussed herein. It is to be understood that notnecessarily all such aspects, advantages or features will be embodied inany particular embodiment of the invention and an artisan wouldrecognize from the disclosure herein a myriad of combinations of suchaspects, advantages or features.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings and the associated descriptions are provided toillustrate embodiments of the present disclosure and do not limit thescope of the claims.

FIG. 1 illustrates an example patient monitoring system that includes aplurality of physiological sensors.

FIG. 2 illustrates a block diagram of an example patient monitoringsystem.

FIG. 3A illustrates an example 3D OCT image obtained from a volar sideof forearm skin.

FIG. 3B illustrates an example one-dimensional distribution of lightintensity versus depth graph obtained by averaging scans of the image ofFIG. 3A.

FIG. 4 illustrates example micro-invasive elements of a bioimpedancesensor.

FIG. 5 illustrates an example bioimpedance sensor.

FIG. 6 shows a graph illustrating various example light intensitysignals acquired at a patient's wrist.

FIG. 7 illustrates a scaled view of the various example light intensitysignals of FIG. 6.

FIG. 8 illustrates an approximation of an intensity of the fluorescenceportion of the light intensity signals of FIG. 7.

FIG. 9 illustrates an approximation of an intensity of the isolatedRaman with tissue absorption signals of FIG. 7.

FIG. 10 illustrates an approximation of an intensity of the isolatedRaman with tissue absorption signals of FIG. 7.

FIGS. 11A-11C illustrate optical scattering differences in skingeometries among various age groups.

FIGS. 12A-12B illustrate an example sensor fusion apparatus configuredwith multiple sensing capabilities that interrogation of the same or asufficiently proximate tissue site.

FIG. 13 illustrates an example reflectance sensor or probe.

FIG. 14 illustrates an environment that shows a hand of a userinteracting with the example reflectance sensor of FIG. 13.

FIG. 15 illustrates an example physiological monitoring system.

FIGS. 16A-16C illustrate an example physiological monitoring apparatus.

FIG. 17 illustrates a flow diagram illustrative of an example routinefor harmonizing data from a plurality of non-invasive sensors.

FIG. 18 illustrates a flow diagram illustrative of an example routinefor harmonizing data from a plurality of non-invasive sensors.

While the foregoing “Brief Description of the Drawings” referencesgenerally various embodiments of the disclosure, an artisan willrecognize from the disclosure herein that such embodiments are notmutually exclusive. Rather, the artisan would recognize a myriad ofcombinations of some or all of such embodiments.

DETAILED DESCRIPTION

Overview

Many non-invasive techniques for determining blood glucose havesignificant shortcomings, such as low accuracy (for example, lessaccuracy than invasive home monitors) and insufficient specificity ofglucose concentration measurement. Accordingly, there is a need for animproved method to non-invasively monitor glucose. Systems and methodsdisclosed herein address various challenges related to non-invasivelydetermining a patient's blood glucose level by harmonizing data frommultiple non-invasive sensors. Each of the non-invasive sensors caninterrogate the same or a similar tissue site of a patient, andvariables identified using one or more sensors can be utilized toimprove data from one or more other sensors. Using these dataharmonization techniques, a glucose concentration measurement can beobtained.

In many instances, a single non-invasive sensor may lack thefunctionality to measure each of the parameters required for an accuratedetermination of an analyte concentration. As a result, manyphysiological monitoring techniques include estimations, such as thosebased on common assumptions, to compensate for the lack of known data.However, due to the sensitivity of analyte measurements, theseestimations can result in inaccurate or unreliable determinations.

For example, Beer's Law (also known as the Beer-Lambert Law) relates theattenuation of light to properties of a material. In particular, Beer'slaw states that absorbance of a material is proportional to theconcentrations of the attenuating species in the material sample. Therelationship between these parameters is expressed in Equation 1 below:A=ε*b*c  (Equation 1)where A is the absorbance of the material at a given wavelength oflight, ε is the molar absorptivity or extinction coefficient (L mol⁻¹cm⁻¹), unique to each molecule and varying with wavelength, b is thelength of the light path through the material (cm), and c is theconcentration of an analyte of interest (mol L⁻¹).

In many cases, the length of the light path through the material(sometimes referred to as the path length) is estimated. For example, ageneric finger may be associated with a first estimated path lengthvalue, while a generic nose may be associated with a second path lengthvalue. However, every person has a unique tissue geometry, which caninclude, but is not limited to, unique skin structure or skin thickness.Furthermore, because tissue is not uniform throughout a person's body,even tissue sites that are close in proximity, such as two differentmeasurements sites on a patient's finger, can have a different tissuegeometry. As noted above, a specific tissue geometry of a particulartissue site can affect the path length value. Accordingly, anon-invasive physiological sensor can be configured to obtain skingeometry data, which can be utilized to calculate a path lengthassociated with a tissue site. In addition or alternatively, the skingeometry data can be utilized to calibrate one or more sensors (forexample, select a focal depth of Raman spectrometer), which can resultin more accurate analytes measurements, such as blood glucoseconcentration measurements.

An optical coherence tomography, or OCT, sensor can be utilized toobtain tissue geometry information. OCT is an optical imaging techniqueusing light waves that produce high-resolution imagery of biologicaltissue. OCT creates its images by interferometrically scanning in deptha linear succession of spots, and measuring backscattered light atdifferent depths in each successive spot. The OCT data can be processedto present an image of the linear cross section. OCT data can beprocessed to determine tissue geometry information, such as skingeometry. For example, the OCT data can provide data regarding athickness of one or more skin layers, such as the epidermis, thedermoepidermal junction, or the dermis.

In addition or alternatively, OCT data can be utilized to determinewhether successive OCT measurements have occurred in the same or adifferent location. For example, one reason data harmonization betweensensors is available relates to the specific optical profile of aparticular tissue site. That is, a particular tissue site retains itsspecific optical profile, and a different measurement location may havea different optical profile. Thus, in many cases, to maintain dataharmonization capabilities, each of the sensors should interrogate thesame or a substantially proximate tissue site. One problem associatedwith interrogating the same or a substantially proximate tissue siterelates to the subsequent placement of a sensor after it has beenremoved from the patient. To solve these and other problems, tissuegeometry information associated with OCT data can be utilized todetermine whether a later one of successive OCT measurements is taken atthe same tissue site as a previous one of the successive OCTmeasurements.

A bio-impedance or tissue dielectric constant sensor can be utilized toobtain tissue geometry information. For example, bio-impedance or tissuedielectric constant data can provide information relating to one or moreskin layers, a hydration of one or more skin layers, or a cellularstructure of the tissue.

Raman spectroscopy has exhibited promise with respect to blood glucosedetection, for example, due to its capability to gain information aboutthe molecular constitution non-invasively. For example, features such aspeaks of the Raman spectra are considered the Raman “fingerprints” ofanalytes such as glucose. Accordingly, using an isolated orsemi-isolated Raman signal, the system can identify physiological data,such as information regarding a patient's blood glucose level.

For various reasons, it has been challenging to isolate a pure Ramansignal from a signal obtained from a Raman spectrometer. For example,emission of fluorescence in tissue often overwhelms any signal collectedfrom the Raman spectrometer, thereby hiding Raman features. In addition,attenuation of the signal due to absorption can further affectprediction of analytes using the collected signal. Furthermore, varyingtissue geometries at tissue sites increases a difficulty in selecting afocal depth of the Raman spectrometer that will optimize a resolution ofthe Raman signal.

Systems and methods disclosed herein address one or more of these orother challenges by utilizing data associated with one or more sensorsto calibrate or improve an accuracy of one or more other sensors. Forexample, a value for path length can be obtained from skin geometrydata, which can improve a pulse oximetry sensor such as a near infrared(NIR), reflectance, or transmittance sensor. As another example, thepresent disclosure addresses various challenges related to leveragingthe Raman scattering signatures for prediction of glucose by harmonizingdata from a plurality of non-invasive physiological sensors. Forinstance, a focal depth of the Raman spectrometer can be selected basedon tissue geometry data, which can improve the Raman spectrometer, andpossibly increase an accuracy of a blood glucose measurement. Similarly,using data from one or more sensor, the Raman signal can be isolated byreducing or removing an effect of Fluorescence on a collected signal, orremoving an effect of attenuation of the signal due to absorption.

System Overview

FIG. 1 illustrates an example patient monitoring system 100 thatincludes a patient monitor 102, a first sensor 104A, and a second sensor104B. In addition, the patient monitoring system 100 can include one ormore other sensors 104N. Sensors 104A, 104B, and 104N can interrogatetissue sites 106A, 106B, and 106N, respectively, of a patient. In somecases, tissue sites 106A, 106B, and 106N can be the same orsubstantially proximate tissue sites, while in other cases one or moreof the tissue sites 106A, 106B, or 106N can be different. Sensor datafrom the sensors 104A, 104B, or 104N can be utilized to determine one ormore physiological parameters or patient vitals. For example, thepatient monitor 102 can receive a signal from the one or more of thesensors 104A, 104B, or 104N and can determine, based on the receivedsignal(s), one or more physiological parameters or one or moremeasurements that can be used to determine a physiological parameter.

The sensors 104A, 104B, and 104N can each be the same type of sensors,or one or more of the sensors 104A, 104B, and 104N can be different fromeach other. For example, the sensors 104A, 104B, and 104N can include,but are not limited to, any combination of an optical coherencetomography (OCT) device, a spectrometer (for example, a Ramanspectrometer), a plethysmograph sensor such as a pulse oximetry device(for example, a near infrared (NIR), reflectance and/or transmittancedevice), a pressure sensor, an electrocardiogram sensor, a bioimpedancesensor, or acoustic sensor, among other sensors.

Two or more of the sensors 104A, 104B, or 104N can be configured tointerrogate the same tissue site. For example, two or more of the senorsensors 104A, 104B, or 104N can be positioned proximate each other suchthat they can interrogate the same tissue, such as a finger, a thumb, athenar space, a hand, a wrist, a forearm, a nose, a limb, a head, anear, a neck, an upper body, or a lower body. In addition oralternatively, two or more of the sensors 104A, 104B, or 104N can beconfigured to interrogate different tissue sites.

In some cases, one or more of the sensors 104A, 104B, or 104N can beintegrated into an apparatus, such as an apparatus that is wearable by auser. For example, one or more of the sensors 104A, 104B, or 104N can beintegrated into a glove that when worn by a user allows the sensor(s) tointerrogate one or more tissue sites. Similarly, one or more of thesensors 104A, 104B, or 104N can be incorporated in or attached tovarious other apparatuses, including, but not limited to, a sock, ashirt, a sleeve, a cuff, a bracelet, a glove, or the like.

In some cases, data from a single sensor 104A, 104B, or 104N does notprovide enough reliable information to determine certain physiologicalparameters. For example, a number of factors can affect an accuracy ofsensor data including, but not limited to, patient movement, sensorplacement, interference, the type of sensor being used, the expansionand contraction of the patient's vascular system, assumptions madeduring calculations, skin temperature, pressure, or the like. Inaddition or alternatively, the determination of some physiologicalparameters (for example, glucose concentration) may require moreinformation than a single sensor can provide.

To solve this and other problems, the patient monitor 102 (or one ormore of the sensors) can harmonize or compare data from two or moresensors, which can allow for a determination of more accurate orreliable data, or can allow for a determination of one or moreadditional physiological parameters, such as blood glucoseconcentration.

As one example, the patient monitor 102 receives a first signal from afirst sensor 104A, the first signal corresponding to an interrogation ofthe first tissue site 106A by the first sensor 104A. Further, thepatient monitor 102 receives a second signal from a second sensor 104B,the second signal corresponding to an interrogation of the second tissuesite 106B by the second sensor 104B. Based on the first signal, thepatient monitor 102 can make adjustments to modify the second sensor orthe second measurement to improve the accuracy or reliability of thesecond sensor or the second measurement. For instance, adjustments caninclude, but are not limited to, adjusting an intensity, power,position, or timing of the second sensor 104 b or adjusting valuescorresponding to the measurement of the second physiological parameter.For example, the patient monitor 102 can modify the second measurementor calculations for a physiological parameter (for example, introduce anoffset, adjust assumed or estimated values, filter a signal, etc.) toaccount for information from the first sensor. In addition oralternatively, the patient monitor can adjust a confidence valueassociated with the first, second, or another measurement.

As described above, based at least in part on the first and secondsignals, the patient monitor 102 can determine a physiologicalparameter. The physiological parameter can be a value which may not beindependently determinable from data from either of the first sensor orthe second sensor alone. For example, data from the first sensor can beutilized to determine a path length, data from the second sensor can beutilized to determine an absorbance, and the physiological parameter caninclude a concentration of an analyte, such as glucose. As anotherexample, data from the first sensor can be utilized to determine a pathlength or absorbance, the second sensor can correspond to a Ramanspectrometer, and the physiological parameter can include aconcentration of an analyte, such as glucose.

The patient monitor 102 can include a digital signal processor (DSP)that receives the signals generated by the one or more sensors 104A,104B, or 104N (for example, through a front-end unit) and determinesparameters, for example, those indicative of the physiological conditionof the patient, using the received signals. The patient monitor 102 can,for example, determine physiological parameters corresponding to thepatient, such as an amount of light absorbed, transmitted through, orreflected at a tissue site, path length (for example, distance thatlight travels through the material), concentration of an analyte,bioimpedance, tissue dielectric constant, pulse rate (PR), pulsepressure variation (PPV), pleth variability index (PVI®), stroke volume(SV), stroke volume variation (SVV), peripheral capillary oxygensaturation (SpO₂), mean arterial pressure (MAP), central venous pressure(CVP), pulse pressure (PP), perfusion index (PI), total hemoglobin(SpHb®), carboxyhemoglobin (SpCO®), methemoglobin (SpMet®), oxygencontent (SpOC®), or acoustic respiration rate (RRa®), among otherparameters. In some aspects, the patient monitor 102 can derive or useone or more relationships (for instance, a set of linear equations) fromtwo or more of the determined parameters. The patient monitor 102 canutilize the one or more relationships to determine the patient's glucoselevels, systemic vascular resistance (SVR), CO, or arterial bloodpressure (BP), among other parameters.

The patient monitor 102 can further compare or analyze one or more ofthe determined parameters (for instance, at least two of the determinedparameters or one determined parameter and a previous or modelparameter) to adjust how a parameter is measured or calculated to makethe measured parameter more accurate or reliable, to adjust a sensor tomake the measured parameter more accurate or reliable, to calculate,derive or determine an accuracy or a confidence value of a measuredparameter, to isolate a parameter, or to determine another parameterbased on the one or more parameters. The sensors, in addition to oralternatively than the patient monitor, can coordinate with each otherto coordinate data or adjust calculations to enhance an accuracy orreliability of measurements. In addition or alternatively, the patientmonitor 102 can use the data to increase an accuracy of one or morecalculations, calculate a previously unknown or estimated physiologicalparameter, calibrate data, or compensate for various circumstances thatmight otherwise result in inaccurate or unreliable data.

Additional Implementations

The patient monitor 102 can be connected to one or more (for instance,three, four, five, or six) sensors, such as the sensors 104A, 104B, or104N, that are detecting from a patient and use the signals receivedfrom the sensors to determine one or more physiological parametersincluding, but not limited to, glucose, SpO₂, PPR, PVI® (for instance,via a palm, thumb or finger plethysmography sensor), SV, MAP, CVP, PP,or PI (for instance, via a palm, thumb or finger plethysmographysensor), among other parameters such as those described herein.

Moreover, the patient monitor 102 can utilize any of the techniquesdescribed herein to determine whether any measurement described herein(using any of the sensors described herein) is valid. The patientmonitor 102 can be configured to show (for example, on a display)information about a valid or invalid measurement, activate an indicatorlight (such as an LED), trigger an alarm, adjust one or more sensors orparameters (for instance, based on a received sensor signal), or displayany data.

The patient monitor 102 can wirelessly or using wires receive, via aninput of the patient monitor 102, a signal from one of the sensors 104A,104B, or 104N. The received signal may take various forms, such as avoltage, a current, or charge. An operational amplifier (op-amp) of thepatient monitor 102 can increase the amplitude, as well as transform thesignal, such as from a current to a voltage. An anti-aliasing filter(AAF) of the patient monitor 102 can then process of the output signalfrom the op-amp to restrict a bandwidth of the output signal from theop-amp to approximately or completely satisfy the sampling theorem overa band of interest. An analog-to-digital convertor (ADC) of the patientmonitor 102 can convert the output signal from the AAF from analog todigital. The output signal from the ADC can then be sampled by a firstprocessor of the patient monitor 102 at a relatively high speed. Theresult of the sampling can next be downsampled by a second processor ofthe patient monitor 102, which may be the same or different from thefirst processor, before waveform analysis may be performed by a DSP.

FIG. 2 illustrates a block diagram of an example patient monitoringsystem 200, which can be an embodiment of the patient monitoring system100. The patient monitoring system 200 can include a patient monitor202, a first non-invasive physiological sensor 204A, a secondnon-invasive physiological sensor 204B, or a third non-invasivephysiological sensor 204C. Furthermore, it should be noted that fewer,additional, or different sensors may be included in patient monitoringsystem 200.

The sensors 204A, 204B, or 204C can respectively detect from tissuesites 206A, 206B, and 206C of a patient. Each of the sensor can measurefrom the same or a similar tissue site. For example, sensor 204A cantake a measurement and sensor 204B can take a subsequent measurement onthe same tissue or at the same location. This may allow the system tomore easily harmonize the data from the sensors or use data from onesensor to improve data or calculation based on another sensor. Thetissue sites 206A, 206B, and 206C can be different. As a non-limitingexample, tissue site 206A can include a thenar space of a patient'shand, and tissue sites 206B, 206C include a thumb of the patient, suchas a base of the thumb. It should be noted, however, that fewer, more ordifferent sensors can be include in system 200.

The DSP 212A can communicate via drivers 216A with the plethysmographysensor 204A and receive via a front-end 214A one or more light intensitysignals indicative of one or more physiological parameters of thepatient or one or more measurements that can be used to determine one ormore physiological parameters. For example, a signal can be indicativeof an intensity of light reflected, refracted, scattered, absorbed, ortransmitted at a tissue site. The drivers 216A can convert digitalcontrol signals into analog drive signals capable of driving emitters209A to illuminate the tissue site 206A. For example, the light emittedby emitters 209A can have an infrared (IR), near infrared (NIR), red,ultra-violet (UV), visible, or other wavelength. The detector(s) 208Acan, in turn, generate one or more composite analog light intensitysignals responsive to light detected by the detector(s) 208A afterattenuation, reflection, refraction, scattering, absorption, etc. at thetissue site 206A. The emitter(s) 209A or detector(s) 208A include afiber-optic component for illumination and collection, respectively. Forexample, the emitter(s) 209A can be positioned on a tissue site 206A(for example, on top, on the bottom, on the side, etc.) and thedetector(s) 208A can be positioned on an opposite portion of the tissuesite 206A.

The front-end 214A can convert the one or more composite analog lightintensity signals from the detector(s) 208A into digital data and inputthe digital data into the DSP 212A. The digital data from the front-end216A can correspond to at least one of a plurality of physiologicalparameters as described herein. For example, the digital data from thefront-end 216A can be representative of a change in the absorption ofparticular wavelengths of light as a function of the changes in thetissue site 206A resulting from pulsing blood.

The DSP 212A can include one or more data or signal processorsconfigured to execute one or more programs for determining physiologicalparameters from input data. The DSP 212A can perform operations thatinclude calculating or outputting one or more physiological measures,such as absorbance, path length, PVI® and other parameters describedherein. The operations performed by the DSP 212A can be implemented insoftware, firmware or other form of code or instructions, or logic orother hardware, or a combination of the above.

The instrument manager 210 can communicate with one or more input oroutput devices 220. The one or more input or output devices 220 caninclude a user interface 222, controls 224, a transceiver 226, and amemory device 228.

The user interface 222 can include a numerical or graphical display thatprovides readouts of measures or parameters, trends and bar graphs ofmeasures or parameters, visual indications of measures or parameters,visual indicators like LEDs of various colors that signify measurementmagnitude, or device management interfaces, which can be generated byLEDs, LCDs, or CRTs, for example. The user interface 222 can include anaudible output device that provides readouts or audible indications ofmeasures or parameters. The user interface 222 can include one or moreinput devices like a keypad, touch screen, pointing device, voicerecognition device, and computer that can be used to supply control orconfiguration data, such as initialization settings, from the userinterface 222 to the instrument manager 210. In some implementations,the user interface 222 can be an interface for devices as well as users.

The controls 224 can be outputs to medical equipment, such as drugadministration devices, ventilators, or fluid IVs, so as to control theamount of administered drugs, ventilator settings, or the amount ofinfused fluids. The patient monitor 202 can use the controls 224 toautomatically treat the patient (for instance, provide fluid to thepatient, provide medication to the patient, turn on a fan to cool thepatient, or adjust a temperature of a room to heat or cool the patient)in response to determining that the patient may benefit from treatment.

The transceiver 226 via an antenna can transmit information aboutoperation of the patient monitor 202 to an electronic device or receivecontrol or configuration data for operating the patient monitor 202. Thetransceiver can, for example, communicate via a computer network orintermediary device or directly with the electronic device usingelectromagnetic radiation.

The memory device 228 can be used to store information about operationof the patient monitor 202. This information can, for example, includereadouts of measures or parameters, trends and bar graphs of measures orparameters, visual indications or indicators.

The DSP 212B can receive via a front-end 214B one or more lightintensity signals indicative of one or more physiological parameters ofthe patient. The drivers 216B can convert digital control signals intoanalog drive signals capable of driving emitters/detectors 220 toilluminate the tissue site 206B. For example, the light emitted byemitters/detectors 220 can be infrared (IR), near infrared (NIR), red,ultra-violet (UV), visible, or other wavelength. The emitters/detectors220 can, in turn, generate one or more composite analog light intensitysignals responsive to light detected by the emitters/detectors 220 lightis reflected, refracted, scattered, absorbed, or attenuated at a tissuesite 206B. The emitters/detectors 220 include a fiber-optic bundle thathas illumination and detection fibers. In addition, for example, asdescribed with respect to FIG. 1, the emitters/detectors 220 can beseparate.

The front-end 214B can convert the one or more composite analog lightintensity signals from the emitters/detectors 220 into digital data andinput the digital data into the DSP 212B. The digital data from thefront-end 214B can correspond to at least one of a plurality ofphysiological parameters, as described herein. The digital data from thefront-end 214B can be representative of a change in theabsorption/reflection of particular wavelengths of light as a functionof the changes in the tissue site 206B resulting from pulsing blood.

The DSP 212B can include one or more data or signal processorsconfigured to execute one or more programs for determining physiologicalparameters from input data. The operations performed by the DSP 212B canbe implemented in software, firmware or other form of code orinstructions, or logic or other hardware, or a combination of the above.

Sensor 204C includes a detector 208C, a light source 209C, a beamsplitter 224C, and a reflector 222C. The light source 209C can emitlight having an approximately equal wavelength, a spectrum ofwavelengths, or a few different wavelengths, for example, two. Forexample, the wavelengths can be selected based on the absorptionspectrum.

As illustrated, light beams from the light source 209C are split usingthe beam splitter 224C into reference arm light beams 230 and sample armlight beams 228. After the light beams 234 are split, the reference armlight beams 230 travel down the reference arm to interact with thereflector 222C, and the sample arm light beams 228 travel down thesample arm to interact with the tissue 206C, for example, from the baseof a patient's thumb.

The tissue site 206C can absorb, reflect, scatter, or refract the samplearm light beams 228. Some of the sample arm light beams 228 arereflected back to the beam splitter 224C. The beam splitter 224C candirect at least some of the reflected sample arm light beams 228 to thedetector 208C.

The light beams traveling down the reference arm interact with areflector 222C and are reflected back to the beam splitter 224C. Similarto the reflected sample arm light beams 228, the reflected reference armlight beams 230 are also directed to the detector 208C by the beamsplitter 224C. Reflected signals from the sample arm and reference armand are presented to photodetector 208C for measurement.

The tissue volume with which the light interacts (referred to as theinteraction volume) can be determined by the spot size of the imagingoptics (surface area) and the coherence length of the light (depth).Thus, the reference arm can determine the depth within the interactionvolume from which scattered light is measured. The patient monitor 200uses the detected signals obtained from the interference of thereflected sample arm light beams 228 and the reflected reference armlight beams 230 to calculate tissue geometry data, such as a skingeometry of one or more skin layers.

Although not illustrated in FIG. 2, imaging optics can also be used tofocus the sample arm light beams 228 prior to interacting with thetissue site 206C. Furthermore, the end of the sample arm and imagingoptics can be placed in close proximity to the tissue site 206C. Thereference arm and reflector 222 are configured such that appropriatewavelength and polarization selected such that the appropriate depth ofthe tissue is measured.

The DSP 212C can receive via a front-end 214C one or more signalsindicative of one or more physiological parameters of the patient, suchas path length. The drivers 216C can convert digital control signalsinto analog drive signals capable of driving emitters 209C to illuminatethe tissue site 206C. The detectors 208C can, in turn, generate one ormore composite analog signals responsive to light detected by thedetectors 208C.

The front-end 214C can convert the one or more composite analog signalsfrom the detectors 208C into digital data and input the digital datainto the DSP 212C. The digital data from the front-end 216C cancorrespond to at least one of a plurality of physiological parameters,as described herein. The DSP 212C can include one or more data or signalprocessors configured to execute one or more programs for determiningphysiological parameters from input data. The operations performed bythe DSP 212C can be implemented in software, firmware or other form ofcode or instructions, or logic or other hardware, or a combination ofthe above.

One or more of the components relating to signal acquisition orprocessing (for example, front end 214A, 214B, 214C, drivers 216A, 216B,216C, DSP 212A, 212B, 212C, etc.) can be incorporated into one or moreconnecting cables, the sensors themselves, or are otherwise closer tothe sensor sites. As such, the patient monitor 202 can include primarilythe input or output devices 220 and the instrument manager 210, (ifappropriate). In addition, some of the components are illustrated asseparate units but can be combined. For instance, front end 214A, 214B,214C can be combined into one or more front ends, drivers 216A, 216B,216C, can be combined into one or more drives, DSP 212A, 212B, 212C canbe combined into one or more DSPs, etc. By reducing the number ofcomponents included in the patient monitor 102, 202, the monitor can besmaller in size or more portable, which can be more convenient for homeor “spot check” use.

Although not illustrated in FIG. 1 or 2 patient monitors 102, 202, orcables connecting the patient monitors to the sensors can furtherinclude one or more outputs that supply the signal(s) from one or moreof the sensors to one or more other electronic devices for furtherprocessing. As one example, the signal(s) from one or more of thesensors can be output in parallel by one or more of the sensors or thecables that couple the one or more sensors to the patient monitor 102,202. In another example, the patient monitors 102, 202 can include oneor more outputs for outputting copy(ies) of the signal(s) from one ormore of the sensors. The copy(ies) of the signal(s) can also be adjustedrelative to the original(s) with filtering, scaling, or other changingprior to being provided to the one or more other electric devices.

Optical Coherence Tomography

Optical coherence tomography, or OCT, is an optical imaging techniqueusing light waves that produces high-resolution imagery of biologicaltissue. OCT creates its images by focusing a beam of light into a mediumand interferometrically scanning the depth of a linear succession ofspots and measuring the absorption and/or the scattering of the light atdifferent depths in each successive spot. In some cases, the data can beprocessed to present an image of the linear cross section of the mediumscanned.

A light source can output a beam of light having a broad spectrum ofwavelengths. The beam of light can be collimated and pass a beamsplitter such that a portion of the beam of light is directed towardsthe tissue and a portion of the beam of light is directed toward areference arm. The light can be either polarized or non-polarized. Apolarizer located on one edge of the beam splitter can polarize thelight linearly, elliptically, or circularly, as desired. The path lengthof the reference arm can be changed based on the desired measurementdepth into the tissue. The wavelength can be centered at, for example,1310 nm with a 50 nm bandwidth. In other cases, the wavelength can be1060 nm with a 70 nm bandwidth. The light source can be selected to havea center wavelength anywhere between 400 nm and 1700 nm with a bandwidthof up to 150 nm. It is understood that different light sources withdifferent bandwidths can be chosen to optimize penetration depth intothe tissue and optimize the depth resolution of sensitivity to skinstructures. The reflected light from the tissue can be collected using aconverging lens and be directed back through the beam splitter to aphotodetector where it is recombined with a portion of the reference armbeam to form an interference pattern. A processor can use the signalsfrom the photodetector to render an image of the tissue.

OCT can provide a non-invasive method for identifying one or morecharacteristics of a tissue's structure. For example, OCT data (whichcan be referred to as tissue geometry data) can include an indication ofa boundary between the main skin layers, such as the epidermis(outermost layer of the skin), the dermis (layer beneath the epidermis),or the hypodermis (layer directly below the dermis and serves to connectthe skin to the underlying fibrous tissue of the bones or muscles). Theepidermis is further divided into five, separate layers (StratumCorneum, Stratum Lucidum, Stratum Granulosum, Stratum Spinosum, andStratum Basale) and the dermis is divided into two, separate layers (thepapillary dermis and the reticular dermis). In some cases, OCT data canprovide an indication of a boundary between any of these layers. Inaddition or alternatively, OCT data can include can include anindication of a thickness of any of the epidermis, dermis, orhypodermis, or their individual layers.

For example, FIG. 3A illustrates an example 3D OCT image obtained from avolar side of forearm skin, and FIG. 3B illustrates an exampleone-dimensional distribution of light intensity vs. depth obtained byaveraging Amplitude scans (A-scans) in the reconstructed OCT 3D image ofFIG. 3A. The slope of the line of FIG. 3B is indicative of index ofrefraction of tissue. A difference in the index of refraction, or adifference in slope, can indicate a new skin or tissue layer becauseeach layer may have a different index of refraction. As illustrated, thefirst peak 302 corresponds to the skin surface 302, and the second peak308 corresponds to the dermoepidermal junction, which is the area oftissue that joins the epidermis 310 and the dermis layers (for example,the papillary dermis 304) of the skin. Accordingly, using OCT data, thesystem 200 can determine a thickness of one or more of various skinlayers such as, but not limited to, the epidermis 310, thedermoepidermal junction, the papillary dermis 304, the reticular dermis306, or the like.

In some cases, OCT data can provide an indication that an OCT sensor isinterrogating an unfavorable tissue site. An unfavorable tissue site caninclude any tissue site that might provide distorted or inaccurate OCTdata (relative to desired OCT data), such as tissue sites that includeat least a portion of a hair follicle, a pore, a bone, a finger- ortoe-nail, a pimple, a mole, a scar, a blister, a callous, debris, otherskin imperfection, or the like.

A particular tissue site can retain its specific optical profile overtime, and that optical profile can be different from another tissuesite. Accordingly, to maintain data harmonization capabilities, it canbe advantageous for sensors to interrogate the same or a substantiallyproximate tissue site. One problem associated with interrogating thesame or a substantially proximate tissue site relates to the subsequentplacement of a sensor after it has been removed from the patient or whenit is shifted in some way from its original positioning. For example, asubsequent OCT measurement or set of measurements can occur minutes,hours, days, weeks, or some other period of time after the firstmeasurement, and it can be unreasonable to require a patient to wear orinteract with the OCT sensor for the duration of that period of time.Nonetheless, even though the OCT sensor has been separated from thepatient or shifted from its original position, it can be advantageousfor the subsequent OCT measurement(s) to occur at the same location asthe first measurement. For example, as described herein, a first tissuesite may have a different tissue structure, density, depth, hydration,analyte concentration, or the like than a second, different tissue site.Thus, if the OCT sensor is placed at the same location for eachmeasurement, then previous calculations, determinations, or the like canbe utilized, which can simplify any calibrations or corrections tosensor data, among other things.

To solve these and other problems, tissue geometry informationassociated with OCT data can be utilized to determine whether asubsequent placement of the OCT sensor allows the OCT sensor tointerrogate the tissue site corresponding to the tissue site of thefirst OCT measurement(s). For example, a processor can compare the firsttissue geometry data associated with the first OCT measurement(s) withthe subsequent tissue geometry data associated with the subsequent OCTmeasurement(s). If the subsequent tissue geometry data does notcorrespond to the first tissue geometry data, then the processor cancause one or more actions to occur. For example, the processor can causean output to indicate that the subsequent tissue geometry data does notcorrespond to the first tissue geometry data. In other words, theprocessor can cause an output to indicate that the subsequent placementof the OCT sensor is incorrect, or is different from the first OCTsensor placement, or the processor can cause an output to indicate aprobe-off condition. In addition or alternatively, the processor cancause the OCT sensor to be re-positioned. For example, based on thecomparison, the processor can suggest a new placement of the OCT sensor,which may more closely correspond to the first placement of the OCTsensor. In addition or alternatively, the processor can control amotorized component to re-position to the OCT sensor such that it moreclosely corresponds to the first placement of the OCT sensor. Still, insome implementations, the processor can calibrate other sensors based onthe subsequent tissue geometry data, rather than the first tissuegeometry data.

Alternatively, if the subsequent tissue geometry data does correspond tothe first tissue geometry data, then the processor can cause one or moreother actions to occur. For example, the processor can cause an outputto indicate that the subsequent tissue geometry data does correspond tothe first tissue geometry data. In other words, the processor can causean output to indicate that the subsequent placement of the OCT sensor iscorrect, as compared to the first placement of the OCT sensor. Inaddition or alternatively, the processor can calibrate other sensorsbased on the first tissue geometry data or the subsequent tissuegeometry data.

Bioelectrical Impedance (Bioimpedance)

Impedance can be characterized as a physical variable describing theresistance characteristics acting on an electric current. Bioelectricalimpedance is based on the principle that tissues or fluids of a patienthave different impedances, that is, opposition to the flow of theelectric current, which in turn may be dependent on variables such aswater and electrolyte content, to name a few. Using a bioelectricalimpedance, analysis can be performed to examine electrical, capacitive,or resistive characteristics of tissue to provide information on anoninvasive basis.

Mathematically, bioelectrical impedance can be represented as a complexnumber including a real component (resistance) and an imaginarydimension (reactance). For example, the bioelectrical impedance can becalculated using the following equation below:Z=R+jX=|Z|e ^(jθ)  (Equation 2)where R is resistance, X is reactance, |Z| is amplitude, and θ is phase.

A number of physiological characteristics or parameters can becalculated or estimated using determined bioelectrical impedancecharacteristics, such as water content, body cell mass (BCM), extracellular mass (ECM), extracellular fluid (ECF), extracellular water(ECW), fat-free mass (FFM), fat mass (FM), total body water (TBW),electrolyte composition, cell membrane mass, cell membrane function andthe like.

Biological tissues can have complex electrical impedance which isdependent, for instance, on the frequency of the electrical appliedfield or tissue cellular structure. Therefore, the electrical impedanceof tissue is a function of its structure and it can be used todifferentiate or determine characteristics of one or more layers totissue.

The system can include a bioimpedance sensor configured to apply anelectrical signal to the tissue, which can include one or more ofvarious voltages, currents, frequencies (for example, 1 kHz to 2.5 GHz),or fields. In some cases, the path length of the signal can vary basedon the applied electrical signal. For example, low frequency signals mayprimarily reflect the extracellular environment, while high frequencysignals may reflect both the intra- and extracellular environment. Inaddition, the bioimpedance sensor can be configured to measurecharacteristics of the applied electrical signal as it passes (or afterit has passed) through tissue. For example, the bioimpedance sensor canmeasure a voltage, current, frequency, magnetic field, etc., which canbe indicative of a voltage difference across tissue or a biologicalimpedance of a tissue, to name a few.

One or more properties of skin may disturb or disrupt bioimpedancemeasurements. For example, the stratum corneum can limit bioimpedancemeasurements. Accordingly, as illustrated in FIG. 4, the bioimpedancesensor can include a micro-invasive element 402 that is configured topenetrate the stratum corneum layer. For example, the bioimpedancesensor can include spikes or other elements that penetrate approximately10-20 μm deep.

FIG. 5 illustrates an example bioimpedance sensor 502. The sensor 502can include multiple channels of spiked regions configured to penetratethe skin. As shown, spacing between the channels can allow for shallowand deep penetration, such that the bioimpedance sensor 502 can measureimpedance at various depths, such as Depths 1 a, 2 a, 2 b, 3 a, or 4 a.

Using information from the bioelectric sensor(s) 502, the system 200 candetermine information about the tissue geometry. For example, based onbioelectric sensor data, the system can determine a cellular structureof the tissue, which may affect various physiological parameters, suchas path length or absorption. In addition, based on bioelectric sensordata, the system can determine information related to hydration of theskin or tissue. For example, water content can be directly related toskin thickness. As described herein, in some cases, the system canselect a focal depth of the Raman spectrometer based at least in part ontissue geometry data.

Tissue Dielectric Constant

In addition or alternatively to bioimpedance or OCT, the system canutilize one or more tissue dielectric constant sensors to determinevarious tissue geometries or tissue information, including, but notlimited to a dielectric constant of tissue. For example, the system 200can include a plurality of probes for different measuring depths, suchas 0.5 mm, 1.5 mm, 2.5 mm, and 5 mm effective depths, and the system candetermine a dielectric value at each of the different depths. Inaddition or alternatively, the system 200 can include one or more probesthat are each configured to measure at different depths, such as 0.5 mm,1.5 mm, 2.5 mm, and 5 mm effective depths, and the system can determinea dielectric value at each of the different depths. The dielectric valuecan correlate with water content, which can be tied to tissue structure.

Accordingly, the tissue dielectric constant can provide informationwhich can be combined with other sensor information (for example, OCT,bioimpedance, reflectance or transmission measurements, Ramanmeasurements) to determine more accurate physiological measurements,such as blood glucose levels. For example, the bioimpedance or tissuedielectric constant data can provide information that correlates withlocal tissue hydration, or can provide information about different skinlayers or cellular structure information. Furthermore, bioimpedance ortissue dielectric constant sensors can provide real-time measurementsthat can provide information about physiological “noise” within thetissue, which can be used to calibrate other measurements orcalculations. As described herein, in some cases, the system can selecta focal depth of the Raman spectrometer based at least in part on tissuegeometry data.

Raman Spectroscopy

The Raman effect is a light-scattering phenomenon that can provideinsight as to one or more characteristics of an analyte in a sample.When light irradiates a tissue, a fraction of the light is scattered,meaning it emerges in directions other than that of the incident(incoming) beam. Most of this scattered light, generally referred to asRayleigh scattering, emerges at the original frequency (f₀) andwavelength of the incident beam. A small portion of the scattered light,however, emerges at some shifted frequency (f_(s)) that is differentfrom, and usually lower than, the original frequency (f₀) and haswavelengths different from that of the incident light. The processleading to this small portion of the scattered light is termed the Ramaneffect or Raman scattering.

Raman scattering can occur with a change in vibrational or rotationalenergy of a molecule. Accordingly, the Raman spectra can containinformation about the specific chemical substance in the irradiatedtissue. For example, Raman scattering yields a set of characteristicpeaks in a spectrum, which is a “fingerprint” of a specific chemicalsubstance. Therefore, Raman has high specificity in glucosemeasurements.

Raman spectroscopy has exhibited promise with respect to blood glucosedetection, for example, due to its capability to gain information aboutthe molecular constitution non-invasively. For example, features (suchas peaks) of the Raman spectra are considered the Raman “fingerprints”of analytes, such as glucose. Accordingly, using an isolated orsemi-isolated Raman signal, the system can identify physiological data,such as information regarding a patient's blood glucose level. However,for various reasons, it has been challenging to isolate a pure Ramansignal from a signal obtained from a Raman spectrometer.

The signal collected through Raman spectroscopy is based at least inpart on the collection optics and the focal distance/depth of the opticsinto the tissue. In some cases, the system can use data from one or moresensors to select an appropriate focal depth. For example, a focal depthcan be selected that may provide a high or the highest resolution of theRaman or collected signal. In addition or alternatively, a focal depthcan be selected that will allow the Raman spectrometer to focus on aparticular location of the tissue, such as the capillary beds. Forexample, OCT, bioelectrical impedance, or tissue dielectric constantmeasurements may provide tissue geometry data (for example, structuraland functional information) that can be used to select a focal depthinto the tissue. For example, the selection can be based at least inpart on a water content of a portion of the tissue, a thickness of oneor more skin layers, or a particular location of tissue, such as thecapillary beds.

Although complex, an approximation of a measurement obtained from aRaman spectrometer can be determined using one or more of the followingequations:I ₁ =I ₀ e ^(−A) ¹   (Equation 3)R ₀ =R _(A) I ₁  (Equation 4)F ₀ =ΦI ₁  (Equation 5)I ₂=Σ((R ₀ +F ₀)e ^(−A) ² )  (Equation 6)where I₀ is an intensity of excitation light, I₁ is an intensity ofscattered light, A₁ is a first interrogation volume, R_(A) representsRaman activity, R₀ is an intensity Raman scattering at a specificwavelength of light, F₀ is an intensity of Florescence at the specificwavelength of light, Φ represents quantum efficiency, A₂ represents asecond interrogation volume, and I₂ is an intensity of measured light.From these relationships, it can be seen that the intensity of measuredlight (I₂) is dependent on the intensity of Raman scattering (R₀), theintensity of Fluorescence (F₀), or the second interrogation volume (A₂),among other things. Due to the nature of the Raman spectroscopy, theintensity of Raman scattering (R₀) is often of very low intensity. Invarious aspects, the system can reduce or remove an effect ofFluorescence or absorption on the measured signal, thereby isolating orimproving the Raman signal (R₀).Fluorescence

A challenge in the implementation of Raman spectroscopy to obtainphysiological data is the emission of fluorescence. Accordingly, iffluorescence is generated, it often overwhelms the Raman signal,effectively hiding the Raman features. Thus, in some cases, is can beadvantageous to isolate the Raman signal.

FIG. 6 shows a graph 600 illustrating various example light intensitysignals acquired at a patient's wrist. In this example, the y-axiscorresponds to arbitrary intensity units, while the x-axis correspondsto a wavenumber shift (in cm⁻¹). Because the Raman signal is dependenton the excitation wavelength, it can be convenient to use wavenumber toindicate the change of wavelength compared to excitation wavelength.Wavelength change is also photo energy change that is often described bywavenumber change in the frequency domain, because wavenumber is used todescribe wavelength in the frequency domain. Wavelength can convert towavenumbers by dividing one centimeter by wavelength.

As described herein, the light intensity signal acquired from a Ramanspectrometer is influenced by the emission of florescence. For example,fluorescence is often much more intense than Raman scattering, andfluorescence can overwhelm or mask a Raman measurement in the lightintensity signal. This can be seen in each of the signals of the graph600. For example, the overall shape of each signal of the graph 600 isattributable to the fluorescence, while the subtle oscillations of eachsignal are attributable to Raman. Because the fluorescence tends to maskthe Raman spectrum, it can be desirable to remove or reduce an effect ofthe fluorescence on the light intensity signal.

Various techniques for removing or reducing an effect of thefluorescence on the light intensity signal are known, including, but notlimited to, confocal configuration, photobleaching, chemical bleaching,deployment of laser excitation at longer wavelengths, filtering withrespect to pixel frequency (or wavenumber frequency), signaldecomposition by various forms of component subtraction from a prioriinformation, photobleaching curve fitting to subtract away anapproximated fluorescence signal, frequency offset Raman methods,spatial offset Raman methods, or the like.

For example, irradiating tissue with intense laser light for a longperiod of time (sometimes referred to as photobleaching) can reduce alevel of fluorescence emission in the light intensity signal, thusincreasing the signal to noise (S/N) ratio of a Raman measurement. Thatis because the fluorescence signal of skin will decrease over time(experiencing an exponential decay) as a source is continually shining,while a Raman signal will not change. By looking at the exponentialdecay (in time) of photobleaching, the system can obtain a fluorescenceapproximation by curve fitting.

As another example, a system can use a first excitation wavelength tocharacterize the fluorescence, and then can subtract the fluorescencefrom a signal of a second excitation wavelength to isolate the Raman.For example, a location of peaks of the fluorescence emission areindependent of excitation wavelength, whereas a location of peaks andcompactness of emission of Raman spectra are dependent on excitationwavelength. Using this information, the system can remove or reduce aneffect of fluorescence emission in the light intensity signal.Fluorescence can also be removed by taking sequential measurements ofthe tissue over time. For example, the fluorescence signal can beisolated by the change of the measured spectrum overtime.

FIG. 7 illustrates a scaled view of the various example light intensitysignals of FIG. 6. As described herein with respect to FIG. 6, the lightintensity signals are influenced by, among other things, fluorescence,Raman scattering, and tissue absorption. For example, the lightintensity signals can include a significant fluorescence baseline.

FIG. 8 illustrates an approximation of an intensity of the fluorescenceportion 800 of the light intensity signals 700 of FIG. 7. Thisapproximation of fluorescence can be determined using varioustechniques, such as those described herein. The system can utilizephotobleaching curve fitting to subtract away an approximatedfluorescence signal. For example, over time, the Raman signal (R₀) willremain constant while the fluorescence F₀ will experience an exponentialdecay. By looking at the exponential decay (in time) of photobleaching,the system can obtain a fluorescence approximation by curve fitting.

FIG. 9 illustrates an approximation of an intensity of the isolatedRaman with tissue absorption signals of FIG. 7. In this example, atleast some of the effect of florescence (for example, illustrated inFIG. 8) has been removed or reduced. Accordingly, the graph 700 of FIG.7 can be approximately equal to the Raman and tissue absorption portion(for example, the Σ(R₀e^(−A) ² ) portion of Equation 6) of the lightintensity signals of FIG. 6. As can be seen from a comparison of FIGS. 7and 9, the presence of fluorescence in the light intensity signals 700can mask many of the Raman features, such as the peaks, valleys,amplitude, compaction, and the like. By removing or reducing thepresence of fluorescence in the light intensity signals 700, the systemcan isolate the Raman signal.

FIG. 10 illustrates an approximation of an intensity of the isolatedRaman with tissue absorption signals of FIG. 7. In this example, thesignal of graph 900 of FIG. 9 has been filtered to reduce or remove atleast some of a remaining effect of florescence. For example, the systemcan filter the signal using a band pass or high pass filter.

Absorption

Another challenge in the implementation of Raman spectroscopy to obtainphysiological data is the attenuation of the signal due to absorption.In some cases, the Raman signal can be isolated or improved by reducingor removing an effect of absorption on the measured signal. For example,sensor data from one or more sensors, such as a near infrared (NIR),reflectance, transmittance, or pulse oximetry sensor, can be utilized todetermine absorption, which can be removed from one or more othermeasurements, such as a Raman measurement.

An effect of the tissue absorption (for example, the e^(−A) portion ofEquation 6) may be removed or reduced in various ways. For example, theabsorption data, transmission data, reflectance data, or the like may bedetermined using data from one or more sensors, such as, but not limitedto, a near infrared (NIR), reflectance, transmittance, or pulse oximetrysensor. Based on the sensor data, a processor can further process thesignal (for example, signal 900 or 1000) to reduce or subtract an effectof the attenuation of the signal due to absorption.

Tissue Geometry

Tissue geometry can vary greatly between individuals. For example, skinstructure or skin thickness can vary across races, ages, or the like.Even individuals having similar demographics can have different skingeometries. FIGS. 11A-11C illustrate optical scattering differences inskin geometries among various age groups. FIG. 11A corresponds to 20-39year olds, FIG. 11B corresponds to 40-59 year-olds, and FIG. 11Ccorresponds to 60-79 year-olds. In these examples, the x-axiscorresponds to a compaction of the skin and is measured from 0 to 200units, where one unit is 3 μm, and the y-axis corresponds to brightness(for example, backscattered intensity) of the skin and is measured from0 to 800 AU (absorbance units). As evidenced by these graphs 1100A,1100B, 1100C, the general skin structure or thickness is not constantthroughout the population.

Tissue geometry can be can also vary greatly between tissue sites of aparticular individual. For example, each of a finger, a thumb, a thenarspace of a hand, a wrist, a forearm, a nose, an ear, a neck, or othertissue site can have a different skin geometry. Even tissue sites thatare in close proximity, such an upper part of a finger and a lower partof a finger, can have a different skin geometry.

Example Sensor Fusion Apparatus

A patient monitoring system such as systems 100 or 200 can includemultiple noninvasive sensors. At least one sensor can be configured toprovide tissue geometry information, and the system can utilize tissuegeometry data to calibrate one or more other sensors or otherwiseimprove data obtained by the one or more other sensors. Techniques forutilizing sensor data to improve or calibrate another sensor can bereferred to as data harmonization or sensor fusion.

As described herein, data acquired by a sensor can be a function of, orat least affected by, the tissue geometry of the particular tissue sitethat the sensor is interrogating. For example, tissues having adifferent geometry can result in a different optical profile.Consequently, data obtained from a first sensor at a first tissue sitemight not be useful for calibrating or improving a sensor that isinterrogating a second, different tissue site. Accordingly, toaccurately or reliably harmonize data between sensors, it can be helpfulfor each of the multiple sensors to acquire data associated with thesame or a similar tissue site. In other words, it can be advantageousfor each of the multiple sensors to interrogate the same or asufficiently proximate tissue site so that a variable or otherinformation determined using data from one sensor can be used to improveone or more others sensors. The present disclosure can provide for anapparatus configured allow multiple sensors to interrogate the same or asufficiently proximate tissue site.

FIGS. 12A-12B illustrate an example sensor fusion apparatus 1200configured with multiple sensing capabilities for interrogation of thesame or a sufficiently proximate tissue site. As shown, the apparatus1200 can include an OCT sensor or lens 1202, a Raman spectrometer 1204,a pulse oximetry sensor 1206, and a bioimpedance sensor 1208.

As illustrated in FIG. 12B, the apparatus 1200 can include a cylindricalhousing 1210. In the illustrated example, the sensor side of theapparatus 1200 can be positioned on or proximate to a tissue site of apatient, and one or more of the an OCT sensor 1202, a Raman spectrometer1204, a pulse oximetry sensor 1206, and a bioimpedance sensor 1208 canbe configured to interrogate the same or a sufficiently proximate tissuesite. Although at least some of the sensors are illustrated as beingconfigured to obtain data via reflectance technologies, in some casesone or more sensors are configured to obtain data via transmittance orother technologies.

Example Reflectance Sensor

FIG. 13 illustrates an example reflectance plethysmography sensor orprobe 1300. The reflectance sensor or probe 1300 includes a light source1302 at its center and seven detector channels 1304 surrounding thelight source 1302. The light source 1302 and emit light to illuminate atissue site, and one or more of the channels 1304 can detect the lightafter it interacts with the tissue site. In some cases, the one or moreof the channels 1304 can generate a composite analog light intensitysignal responsive to the detected light. In some cases, light source1302 or the channels 1304 can include a fiber-optic component forillumination or collection. For example, the light source 1302 caninclude a fiber bundle.

FIG. 14 illustrates an environment 1400 that shows a hand of a userinteracting with the example reflectance sensor 1300 of FIG. 13. Thereflectance sensor 1300 can be configured to interact with one or moreof the various tissue sites described herein. For example, asillustrated, the reflectance sensor 1300 can be positioned tointerrogate at a metacarpal bone 1306. The metacarpal bone 1306 formsthe intermediate part of the skeletal hand located between the phalangesof the fingers and the carpal bones of the wrist which forms theconnection to the forearm.

Example Patient Monitoring

FIG. 15 illustrates an example physiological monitoring system 1500,which can be an embodiment of the patient monitoring system 100 or 200.As illustrated in FIG. 15, the system 1500 can include a first sensor1504A and a second sensor 1504B. In some implementations, the firstsensor 1504A, the second sensor 1504B, or another sensor can beintegrated into an apparatus, such as a wearable apparatus like a glove,a sock, an armband, a headband, a chest strap, etc.

The first sensor 1504A can be similar to sensor 204A, as describedherein with respect to FIG. 2. For example, the sensor 1504A can includean emitter and detector. The emitter can emit light (for example, of aninfrared or near-infrared wavelength) to illuminate a tissue site of apatient. In this example, the tissue site corresponds to a thenar spaceof the patient's hand. However, other tissue sites are contemplated. Asthe light interacts with (for example, passes through) the thenar spaceof the hand, some light may absorbed, reflected, refracted, or the like.The detector can receive or generate a signal responsive to the lightdetected by the detector after it interacts with the thenar space of thehand. The signal generated by the detector can be received by aprocessor (not shown), which can determine one or more variousphysiological parameters, such as an absorbance of the tissue based atleast in part on the received signal.

The second sensor 1504B can be similar to sensor 204C, as describedherein with respect to FIG. 2. For example, the second sensor 1504B caninclude a light source and a detector. The emitter can emit light (forexample, of an infrared or near-infrared wavelength) to illuminate atissue site of a patient. As the light interacts with (for example,reflects off) the thenar space of the hand, some light may absorbed,transmitted through, reflected, refracted, or the like. The detector canreceive or generate a signal responsive to the light detected by thedetector after it interacts with the hand. The signal generated by thedetector can be received by a processor (not shown), which can determineone or more various physiological parameters, such as a transmittance ofthe tissue based at least in part on the received signal.

FIGS. 16A-16C illustrate an example physiological monitoring apparatus1600. As illustrated, a user can place his or her arm in the apparatus1600, such that two or more sensors of the apparatus 1600 caninterrogate tissue of the arm. The two or more sensors can correspond toany of the sensors described herein. For example, the two or moresensors can interrogate the same or a different tissue site of the arm.In some cases, the apparatus 1600 can be miniaturized and integratedinto a wearable apparatus, such as a glove, a sock, an armband, aheadband, a chest strap, etc.

Example Data Harmonization

FIG. 17 illustrates a flow diagram illustrative of an example routinefor harmonizing data from a plurality of non-invasive sensors. Oneskilled in the relevant art will appreciate that the elements outlinedfor routine 1700 may be implemented by one or many computingdevices/components, such as in hardware, with a front end component,with a sensor interface, or with a processor, such as one or moreprocessors housed in a patient monitor, one or more remote processors,one or more processors housed in the sensors, etc. Accordingly, althoughroutine 1700 has been logically associated as being generally performedby a processor, the following illustrative embodiments should not beconstrued as limiting.

At block 1702, a processor can receive data from one or more firstnoninvasive sensors. The one or more first noninvasive sensors caninclude an optical coherence tomography (OCT) sensor. As describedherein, the OCT sensor can provide a non-invasive method for identifyingone or more characteristics of a tissue's structure. The data receivedby the processor from the OCT sensor can include OCT data, which can bereferred to as tissue geometry data.

In addition or alternatively, the one or more first noninvasive sensorscan include a bioimpedance sensor or a tissue dielectric constantsensor. As described herein, the bioimpedance sensor or tissuedielectric constant sensor can provide a non-invasive method foridentifying one or more characteristics of a tissue's structure. Thedata received by the processor from the bioimpedance sensor or tissuedielectric constant sensor can include bioimpedance data, which caninclude tissue geometry data, hydration data, or the like.

At block 1704, a processor can receive data from one or more secondnoninvasive sensors. The one or more second noninvasive sensors caninclude a pulse oximetry sensor, such as a reflectance or transmissionsensor. As described herein, the pulse oximetry sensor can provide anon-invasive method for identifying or more of various physiologicalparameters.

At block 1706, a processor can receive data from one or more thirdnoninvasive sensors. The one or more second noninvasive sensors caninclude a Raman spectrometer. As described herein, the Ramanspectrometer can provide a non-invasive method for identifying or moreof various physiological parameters.

At block 1708, the processor can harmonize the data received from two ormore of the non-invasive sensors. By harmonizing the data from two ormore non-invasive sensors, the system may be able to compensate forcircumstances that might otherwise result in inaccurate or unreliabledata. For example, using skin geometry information (for example, skinthickness), the processor can weight or prioritize longer or shorterpath length detectors. In addition or alternatively, the various sensordata, such as skin geometry information, can allow the processorcompensate for sensor or probe placement. For example, a location,coupling, or pressure can be compensated by the processor by adjustingpath length, which can be determined from the various sensor data, suchas skin geometry information. Similarly, the processor can utilize thevarious sensor data, such as skin geometry information, to detect driftor motion at the tissue site.

As a non-limiting example, the data received at block 1702 from the OCTsensor, the bioelectrical impedance sensor, or the tissue dielectricconstant sensor can include tissue geometry information. Based at leastin part on the tissue geometry data, the processor can determine a pathlength corresponding to a tissue site interrogated by the one or morefirst noninvasive sensors. In some cases, the determined path length canbe utilized with the pulse oximetry sensor to determine a concentrationof an analyte, such as blood glucose. For example, based on the datareceived at block 1704 from the one or more second noninvasive sensors,the processor can determine an absorbance corresponding to a tissue siteinterrogated by the one or more second noninvasive sensors. Using one ormore relationships derived from Beer's law (Equation 1), theconcentration, c, of one or more analytes can be determined using theabsorbance, A, determined from the pulse oximetry sensor data, and thepath length, b, determined from the tissue geometry data.

As another non-limiting example, the processor can utilize the tissuegeometry data to select a focal depth or focal length, wavelength,refractive index, or other parameter associated with the Ramanspectrometer. For example, the tissue geometry data can provide anindication of a particular location of tissue, such as the capillarybeds. The processor can select a focal depth or focal length of theRaman spectrometer such that the Raman spectrometer can focus on thisparticular location. As a result, the processor can determine a moreaccurate indication of glucose concentration from the Raman signal.

As another non-limiting example, the processor can utilize the pulseoximetry data to filter data received from a Raman Spectrometer toisolate a Raman Spectra. For example, as described herein, a directmeasurement of glucose can be determined based on features of theisolated Raman signal. Using the pulse oximetry data, the processor canfilter out an effect of absorbance on the Raman Spectra.

In addition or alternatively, using the various sensor data, theprocessor can create calibrations for one or more individuals. Forexample, although skin geometry may vary between individuals, one ormore groups of individuals may have similar skin geometries, which canallow for more accurate physiological parameter estimations of forindividuals in those groups. For example, using the various sensor data,such as the skin geometry, Raman, or NIR data, the processor candetermine calibrations for different groups, such as different skinpopulations, different ages, or the like.

The various blocks of process 1700 described herein can be implementedin a variety of orders, and that the system can implement one or more ofthe blocks concurrently or change the order, as desired. For example,the system 100 can concurrently receive any of the sensor data, orreceive the sensor data in any order. Similarly, the system can make oneor more calculations or determinations in any order, such as before orafter receiving data from one or more sensors.

It will be understood that any of the first, second, or third sensorscan interrogate the same or a different tissue site. Furthermore, itwill be understood that fewer, more, or different blocks can be used aspart of the routine 1700. Likewise, fewer, more, or different sensorscan be used by the system. For example, the routine 1700 can includeblocks for receiving data associated with additional non-invasivesensors or determining various other physiological parameters.Furthermore, the routine 1700 can include causing a display to displayone or more of various indications of the any other the sensor data,calculations, or determinations.

FIG. 18 illustrates a flow diagram illustrative of an example routinefor harmonizing data from a plurality of non-invasive sensors. Oneskilled in the relevant art will appreciate that the elements outlinedfor routine 1800 may be implemented by one or many computingdevices/components, such as in hardware, with a front end component,with a sensor interface, or with a processor, such as one or moreprocessors housed in a patient monitor, one or more remote processors,one or more processors housed in the sensors, etc. Accordingly, althoughroutine 1800 has been logically associated as being generally performedby a processor, the following illustrative embodiments should not beconstrued as limiting.

At block 1802, the process 1800 can receive tissue geometry data from afirst noninvasive sensor. As described herein, the first non-invasivesensor can include a combination of one or more of an OCT sensor, abioimpedance sensor, a tissue dielectric constant sensor, or any othersensor configured to measure or determine tissue geometry data. Thetissue geometry data can include various information corresponding tothe skin, fluids, bones, or the like. For example, tissue geometry datacan include, but is not limited to, a thickness of one or more skinlayers (for example, the epidermis, the dermoepidermal junction, thepapillary dermis, the reticular dermis, etc.), cellular structureinformation, a water content of a portion of the tissue, etc.

At block 1804, the process 1800 can calibrate a Raman Spectrometer basedat least in part on the tissue geometry data received at block 1802. Forexample, the tissue geometry data can provide insight about the tissuesite, which can allow the process 1800 to optimize one or more settingsof the Raman spectrometer. For example, based at least in part on thetissue geometry data, the process 1800 can select a focal depth or focallength, wavelength, refractive index, or other parameter associated withthe Raman spectrometer. By adjusting one or more settings or positioningof the Raman spectrometer based on the tissue geometry data, the processcan enhance a signal received by the Raman spectrometer. For example,the new settings can increase the collection efficiency, the resolution,the signal-to-noise ratio, or the like of the Raman signal.

At block 1806, the process 1800 can receive absorption, transmission,reflectance, or other data from a second noninvasive sensor. Asdescribed herein, the second non-invasive sensor can include one or moreof a pulse oximetry sensor, a reflectance sensor, a transmittancesensor, or another sensor from which absorption, transmission,reflectance, or other tissue related data can be determined. In somecases, the second noninvasive sensor can include a light sourceconfigured to emit light and a detector and configured to detect light.Depending on the type of sensors, the detected can be configured todetect light after having it has passed through, reflected, refracted,or scattered at a tissue site of a patient. In some cases, the tissuesite corresponding to the second sensor (for example, the tissue site atwhich the second sensor takes a measurement) is the same tissue site (orwithin a close proximity) as the tissue site of the second sensor. Forexample, the first and second sensors can be configured to interrogatethe tissue site at different periods of time. However, in some cases,the first and second sensors can be configured to interrogate differenttissue sites.

At 1808, the process 1800 can receive a Raman signal corresponding tothe Raman spectrometer. As described herein, the light intensity signalacquired from a Raman spectrometer is influenced by the emission offlorescence.

At block 1810, the process 1800 can determine an isolated Raman signalby reducing or removing an effect of fluorescence or an effect ofabsorption from the Raman signal received at block 1808. As describedherein, fluorescence can overwhelm or mask a Raman measurement in thelight intensity signal. As such, the process 1800 can use one or moretechniques described herein to reduce or remove an effect of thefluorescence on the Raman signal. In addition or alternatively, theprocess 1800 can reduce or remove an effect of absorption on the Ramansignal. For example, using the absorption data acquired at block 1806,the process 1800 can filter, subtract, reduce, or remove an effect ofabsorption on the Raman signal. By reducing or removing an effect offluorescence or an effect of absorption from the Raman signal, theprocess 1800 can determine an isolated (or semi-isolated) Raman signal.

At block 1812, the process 1800 can determine data corresponding to oneor more analytes based at least in part on the isolated Raman signal.For example, features of the Raman spectra (such as peaks, valleys,concentrations, etc.) can corresponds to analytes such as glucose.Accordingly, using the isolated or semi-isolated Raman signal, thesystem can identify physiological data, such as information regarding apatient's blood glucose level. Thus, the process 1800 can harmonize datafrom various non-invasive sensors to non-invasively determine apatient's blood glucose level, or other analyte.

It will be understood that the various blocks of process 1800 describedherein can be implemented in a variety of orders, and that the systemcan implement one or more of the blocks concurrently or change theorder, as desired. Furthermore, it will be understood that fewer, more,or different blocks can be used as part of the routine 1800. Forexample, fewer, more, or different sensors can be used by the system.Furthermore, the routine 1800 can include blocks for receiving dataassociated with additional non-invasive sensors or determining variousother physiological parameters. Furthermore, the routine 1800 caninclude displaying one or more of various indications of the any otherthe sensor data, calculations, or determinations.

Further Examples

Various example features can be found in the following clauses, whichcan be implemented together with any combination of the featuresdescribed above:

Clause 1: A physiological monitoring system configured to determine aphysiological parameter by harmonizing data between two or moredifferent types of non-invasive physiological sensors interrogating thesame or proximate measurement sites, the physiological monitoring systemcomprising:

-   -   a first non-invasive sensing device of a first type configured        to interrogate a tissue site of a patient and generate a first        signal indicative of first physiological data associated with        the tissue site;    -   a second non-invasive sensing device of a second type that is        different from the first type configured to interrogate the        tissue site of the patient and generate a second signal        indicative of second physiological data associated with the        tissue site; and    -   one or more processors in communication with the first and        second non-invasive sensing devices, the one or more processors        configured to:        -   receive the first signal indicative of the first            physiological data;        -   receive the second signal indicative of the second            physiological data; and        -   determine a physiological parameter based at least in part            on the first and second signals.

Clause 2: The system of Clause 1, wherein the first non-invasive sensingdevice comprises one or more of an optical coherence tomography (OCT)sensor, a bioimpedance sensor, or a tissue dielectric constant sensor.

Clause 3: The system of any of the previous clauses, wherein the firstphysiological data comprises tissue geometry data corresponding to thetissue site.

Clause 4: The system of any of the previous clauses, wherein tissuegeometry data comprises at least one of data corresponding to one ormore of a thickness of one or more layers of skin of the tissue site,cellular structure information associated with the tissue site, or awater content associated with the tissue site.

Clause 5: The system of any of the previous clauses, wherein the firstnon-invasive sensing device comprises a plethysmography sensor.

Clause 6: The system of any of the previous clauses, wherein theplethysmography sensor comprises one or more of a pulse oximetry sensor,a transmission plethysmography sensor, or a reflectance plethysmographysensor.

Clause 7: The system of any of the previous clauses, wherein theplethysmography sensor comprises:

at least one emitter configured to emit light, and

at least one detector configured to:

-   -   detect the light after interaction with the tissue site, and    -   generate the second signal responsive to the detected light.

Clause 8: The system of any of the previous clauses, wherein the firstnon-invasive sensing device and the second non-invasive sensing deviceare configured to simultaneously interrogate the tissue site.

Clause 9: The system of any of the previous clauses, wherein the firstnon-invasive sensing device and the second non-invasive sensing deviceare configured to interrogate the tissue site at distinct and differenttime periods.

Clause 10: The system of any of the previous clauses, wherein the secondnon-invasive sensing device comprises a Raman spectrometer.

Clause 11: The system of any of the previous clauses, wherein the secondphysiological data corresponds to Raman spectra associated with thetissue site.

Clause 12: The system of any of the previous clauses, wherein the one ormore processors are further configured to determine an isolated Ramansignal by reducing or removing at least one of an effect of fluorescenceor an effect of absorption from the second signal.

Clause 13: The system of any of the previous clauses, wherein the one ormore processors are further configured to determine the isolated Ramansignal based at least in part on the first signal.

Clause 14: The system of any of the previous clauses, wherein the one ormore processors are further configured to determine a path lengthassociated with the tissue site based at least in part on the firstsignal, wherein the determination of the physiological parameter isbased at least in part on the determined path length.

Clause 15: The system of any of the previous clauses, wherein the one ormore processors are further configured to determine an absorption oflight of the tissue site based at least in part on the second signal,wherein the determination of the physiological parameter is based atleast in part on the determined absorption of light.

Clause 16: The system of any of the previous clauses, whereinphysiological parameter comprises a concentration of one or moreanalytes.

Clause 17: The system of any of the previous clauses, whereinphysiological parameter comprises a blood glucose concentrationassociated with the tissue site.

Clause 18: The system of any of the previous clauses, wherein one ormore processors are further configured to calibrate the secondnon-invasive sensing device based at least in part on the first signal.

Clause 19: The system of any of the previous clauses, wherein secondnon-invasive sensing device comprises a Raman spectrometer, wherein tocalibrate the Raman spectrometer, the one or more processors areconfigured to select at least one of a focal depth, a focal length, awavelength, or a refractive index, associated with the Ramanspectrometer.

Clause 20: The system of any of the previous clauses, wherein the tissuesite comprises a thenar space of a hand.

Clause 21: The system of any of the previous clauses, wherein the tissuesite comprises an area associated with a metacarpal bone.

Clause 22: A method for determining a physiological parameter byharmonizing data between two or more different types of non-invasivephysiological sensors interrogating the same or proximate measurementsites, the method comprising:

-   -   receiving a first signal from a first non-invasive sensing        device of a first type, wherein the first non-invasive sensing        device is configured to interrogate a tissue site of a patient        and generate the first signal, wherein the first signal is        indicative of first physiological data associated with the        tissue site;    -   receiving a second signal from a second non-invasive sensing        device of a second type that is different from the first type,        wherein the second non-invasive sensing device is configured to        interrogate the tissue site of the patient and generate the        second signal, wherein the second signal is indicative of second        physiological data associated with the tissue site; and/or        determining a physiological parameter based at least in part on        the first and second signals.

Clause 23: The method of Clause 22, wherein the first non-invasivesensing device comprises one or more of an optical coherence tomography(OCT) sensor, a bioimpedance sensor, or a tissue dielectric constantsensor.

Clause 24: The method of any of Clauses 22 or 23, wherein the firstphysiological data comprises tissue geometry data corresponding to thetissue site.

Clause 25: The method of any of Clauses 22-24, wherein tissue geometrydata comprises at least one of data corresponding to one or more of athickness of one or more layers of skin of the tissue site, cellularstructure information associated with the tissue site, or a watercontent associated with the tissue site.

Clause 26: The method of Clauses 22-25, wherein the first non-invasivesensing device comprises a plethysmography sensor.

Clause 27: The method of Clauses 22-26, wherein the plethysmographysensor comprises one or more of a pulse oximetry sensor, a transmissionplethysmography sensor, or a reflectance plethysmography sensor.

Clause 28: The method of Clauses 22-27, wherein the plethysmographysensor comprises:

at least one emitter configured to emit light, and

at least one detector configured to:

-   -   detect the light after interaction with the tissue site, and    -   generate the second signal responsive to the detected light.

Clause 29: The method of Clauses 22-28, wherein the first non-invasivesensing device and the second non-invasive sensing device are configuredto simultaneously interrogate the tissue site.

Clause 30: The method of Clauses 22-29, wherein the first non-invasivesensing device and the second non-invasive sensing device are configuredto interrogate the tissue site at distinct and different time periods.

Clause 31: The method of Clauses 22-30, wherein the second non-invasivesensing device comprises a Raman spectrometer.

Clause 32: The method of Clauses 22-31, wherein the second physiologicaldata corresponds to Raman spectra associated with the tissue site.

Clause 33: The method of Clauses 22-32, further comprising determiningan isolated Raman signal by reducing or removing at least one of aneffect of fluorescence or an effect of absorption from the secondsignal.

Clause 34: The method of Clauses 22-33, wherein said determining theisolated Raman signal is based at least in part on the first signal.

Clause 35: The method of Clauses 22-34, further comprising determining apath length associated with the tissue site based at least in part onthe first signal, wherein said determining the physiological parameteris based at least in part on the determined path length.

Clause 36: The method of Clauses 22-35, further comprising determiningan absorption of light of the tissue site based at least in part on thesecond signal, wherein said determining the physiological parameter isbased at least in part on the determined absorption of light.

Clause 37: The method of Clauses 22-36, wherein physiological parametercomprises a concentration of one or more analytes.

Clause 38: The method of Clauses 22-37, wherein physiological parametercomprises a blood glucose concentration associated with the tissue site.

Clause 39: The method of Clauses 22-38, further comprising calibratingthe second non-invasive sensing device based at least in part on thefirst signal.

Clause 40: The method of Clauses 22-39, wherein second non-invasivesensing device comprises a Raman spectrometer, the method furthercomprising calibrating the Raman spectrometer by selecting at least oneof a focal depth, a focal length, a wavelength, or a refractive index,associated with the Raman spectrometer.

Clause 41: The method of Clauses 22-40, wherein the tissue sitecomprises a thenar space of a hand.

Clause 42: The method of Clauses 22-41, wherein the tissue sitecomprises an area associated with a metacarpal bone.

Clause 43: A physiological monitoring device comprising any of thefeatures of any of the previous clauses.

Terminology

The term “and/or” herein has its broadest least limiting meaning whichis the disclosure includes A alone, B alone, both A and B together, or Aor B alternatively, but does not require both A and B or require one ofA or one of B. As used herein, the phrase “at least one of” A, B, “and”C should be construed to mean a logical A or B or C, using anon-exclusive logical or.

The following description is merely illustrative in nature and is in noway intended to limit the disclosure, its application, or uses. Forpurposes of clarity, the same reference numbers will be used in thedrawings to identify similar elements. It should be understood thatsteps within a method may be executed in different order withoutaltering the principles of the present disclosure.

Features, materials, characteristics, or groups described in conjunctionwith a particular aspect, embodiment, or example are to be understood tobe applicable to any other aspect, embodiment or example describedherein unless incompatible therewith. All of the features disclosed inthis specification (including any accompanying claims, abstract anddrawings), or all of the steps of any method or process so disclosed,may be combined in any combination, except combinations where at leastsome of such features or steps are mutually exclusive. The protection isnot restricted to the details of any foregoing embodiments. Theprotection extends to any novel one, or any novel combination, of thefeatures disclosed in this specification (including any accompanyingclaims, abstract and drawings), or to any novel one, or any novelcombination, of the steps of any method or process so disclosed.

While certain embodiments have been described, these embodiments havebeen presented by way of example only, and are not intended to limit thescope of protection. Indeed, the novel methods and systems describedherein may be embodied in a variety of other forms. Furthermore, variousomissions, substitutions and changes in the form of the methods andsystems described herein may be made. Those skilled in the art willappreciate that in some embodiments, the actual steps taken in theprocesses illustrated or disclosed may differ from those shown in thefigures. Depending on the embodiment, certain of the steps describedabove may be removed, others may be added. For example, the actual stepsor order of steps taken in the disclosed processes may differ from thoseshown in the figures. Depending on the embodiment, certain of the stepsdescribed above may be removed, others may be added. For instance, thevarious components illustrated in the figures may be implemented assoftware or firmware on a processor, controller, ASIC, FPGA, ordedicated hardware. Hardware components, such as processors, ASICs,FPGAs, and the like, can include logic circuitry. Furthermore, thefeatures and attributes of the specific embodiments disclosed above maybe combined in different ways to form additional embodiments, all ofwhich fall within the scope of the present disclosure.

User interface screens illustrated and described herein can includeadditional or alternative components. These components can includemenus, lists, buttons, text boxes, labels, radio buttons, scroll bars,sliders, checkboxes, combo boxes, status bars, dialog boxes, windows,and the like. User interface screens can include additional oralternative information. Components can be arranged, grouped, displayedin any suitable order.

Although the present disclosure includes certain embodiments, examplesand applications, it will be understood by those skilled in the art thatthe present disclosure extends beyond the specifically disclosedembodiments to other alternative embodiments or uses and obviousmodifications and equivalents thereof, including embodiments which donot provide all of the features and advantages set forth herein.Accordingly, the scope of the present disclosure is not intended to belimited by the specific disclosures of preferred embodiments herein, andmay be defined by claims as presented herein or as presented in thefuture.

Conditional language, such as “can,” “could,” “might,” or “may,” unlessspecifically stated otherwise, or otherwise understood within thecontext as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements, or steps. Thus, such conditional language is notgenerally intended to imply that features, elements, or steps are in anyway required for one or more embodiments or that one or more embodimentsnecessarily include logic for deciding, with or without user input orprompting, whether these features, elements, or steps are included orare to be performed in any particular embodiment. The terms“comprising,” “including,” “having,” and the like are synonymous and areused inclusively, in an open-ended fashion, and do not excludeadditional elements, features, acts, operations, and so forth. Also, theterm “or” is used in its inclusive sense (and not in its exclusivesense) so that when used, for example, to connect a list of elements,the term “or” means one, some, or all of the elements in the list.Further, the term “each,” as used herein, in addition to having itsordinary meaning, can mean any subset of a set of elements to which theterm “each” is applied.

Conjunctive language such as the phrase “at least one of X, Y, and Z,”unless specifically stated otherwise, is otherwise understood with thecontext as used in general to convey that an item, term, etc. may beeither X, Y, or Z. Thus, such conjunctive language is not generallyintended to imply that certain embodiments require the presence of atleast one of X, at least one of Y, and at least one of Z.

Language of degree used herein, such as the terms “approximately,”“about,” “generally,” and “substantially” as used herein represent avalue, amount, or characteristic close to the stated value, amount, orcharacteristic that still performs a desired function or achieves adesired result. For example, the terms “approximately”, “about”,“generally,” and “substantially” may refer to an amount that is withinless than 10% of, within less than 5% of, within less than 1% of, withinless than 0.1% of, and within less than 0.01% of the stated amount. Asanother example, in certain embodiments, the terms “generally parallel”and “substantially parallel” refer to a value, amount, or characteristicthat departs from exactly parallel by less than or equal to 15 degrees,10 degrees, 5 degrees, 3 degrees, 1 degree, or 0.1 degree.

The scope of the present disclosure is not intended to be limited by thespecific disclosures of preferred embodiments in this section orelsewhere in this specification, and may be defined by claims aspresented in this section or elsewhere in this specification or aspresented in the future. The language of the claims is to be interpretedbroadly based on the language employed in the claims and not limited tothe examples described in the present specification or during theprosecution of the application, which examples are to be construed asnon-exclusive.

What is claimed is:
 1. A physiological monitoring system configured todetermine a physiological parameter by harmonizing data between two ormore different types of non-invasive physiological sensors interrogatinga shared tissue volume, the physiological monitoring system comprising:a plethysmography sensor configured to generate a first signalindicative of first physiological data associated with a shared tissuevolume; a second non-invasive sensing device configured to generate asecond signal indicative of tissue geometry data associated with theshared tissue volume, wherein the second non-invasive sensing devicecomprises at least one of, an optical coherence tomography (OCT) sensor,a tissue dielectric constant sensor, or a bioimpedance sensor; and oneor more processors in communication with the plethysmography sensor andthe second non-invasive sensing device, the one or more processorsconfigured to: receive the first signal and the second signal, determinea physiological parameter based at least in part on the first signal andthe second signal, and output a visual or audible indication of thephysiological parameter.
 2. The system of claim 1, wherein tissuegeometry data comprises information related to a thickness of one ormore layers of skin of the shared tissue volume, a cellular structure ofthe shared tissue volume, or a water content of the shared tissuevolume.
 3. The system of claim 1, wherein the plethysmography sensor andthe second non-invasive sensing device concurrently interrogate theshared tissue volume.
 4. The system of claim 1, wherein theplethysmography sensor and the second non-invasive sensing deviceinterrogate the shared tissue volume at distinct and different timeperiods.
 5. The system of claim 1, further comprising a Ramanspectrometer configured to generate a third signal corresponding toRaman spectra associated with the shared tissue volume.
 6. The system ofclaim 5, wherein the one or more processors are further configured todetermine an isolated Raman signal by reducing or removing at least oneof an effect of fluorescence or an effect of absorption from the thirdsignal.
 7. The system of claim 1, wherein the one or more processors arefurther configured to determine a path length associated with the sharedtissue volume, wherein the physiological parameter is determined basedat least in part on the path length associated with the shared tissuevolume.
 8. The system of claim 1, wherein the one or more processors arefurther configured to determine an absorption of light of the sharedtissue volume, wherein the physiological parameter is determined basedat least in part on the absorption of light of the shared tissue volume.9. The system of claim 1, wherein the physiological parameter comprisesa blood glucose concentration associated with the shared tissue volume.10. The system of claim 1, further comprising a Raman spectrometer,wherein the one or more processors are further configured to calibratethe Raman spectrometer based at least in part on the first signal,wherein to calibrate the Raman spectrometer, the one or more processorsare configured to select at least one of a focal depth, a focal length,a wavelength, or a refractive index, associated with the Ramanspectrometer.
 11. A method for determining a physiological parameter byharmonizing data between two or more different types of non-invasivephysiological sensors interrogating a shared tissue volume, the methodcomprising: receiving a first signal from a plethysmography sensorconfigured to interrogate a shared tissue volume and generate the firstsignal, wherein the first signal is indicative of first physiologicaldata associated with the shared tissue volume; receiving a second signalfrom a bioimpedance sensor, wherein the second signal is indicative ofsecond physiological data associated with the shared tissue volume;determining a physiological parameter based at least in part on thefirst and second signals; and outputting a visual or audible indicationof the physiological parameter.
 12. The method of claim 11, wherein theplethysmography sensor and the bioimpedance sensor concurrentlyinterrogate the shared tissue volume.
 13. The method of claim 11,further comprising receiving a third signal from a tissue dielectricconstant sensor, wherein the third signal is indicative of thirdphysiological data associated with the shared tissue volume, and whereinsaid determining the physiological parameter is further based at leastin part on the third signal.
 14. The method of claim 11, furthercomprising receiving a third signal from a Raman spectrometer, whereinthe third signal is indicative of third physiological data associatedwith the shared tissue volume, and wherein said determining thephysiological parameter is further based at least in part on the thirdsignal.
 15. A physiological monitoring system configured to determine aphysiological parameter by harmonizing data between two or moredifferent types of non-invasive physiological sensors interrogating ashared volume of tissue, the physiological monitoring system comprising:a plurality of sensors configured to interrogate a shared volume oftissue of a patient, wherein each of the plurality of sensors isconfigured to generate a sensor signal associated with physiologicaldata of the shared volume of tissue, wherein the plurality of sensorscomprises a plethysmography sensor and a bioimpedance sensor; and aprocessor in communication with each of the plurality of sensors andconfigured to: receive the plurality of sensor signals, determine aphysiological parameter associated with the shared volume of tissuebased at least in part on the plurality of sensor signals, and output avisual or audible indication of the physiological parameter.
 16. Thesystem of claim 13, wherein the plurality of sensors further comprises atemperature sensor, a Raman sensor, an optical coherence tomography(OCT) sensor, and a tissue dielectric constant sensor.
 17. The system ofclaim 13, wherein the plurality of sensors comprises at least fourdifferent types of sensors.
 18. The system of claim 13, wherein at leasttwo of the plurality of sensors are configured to concurrentlyinterrogate the shared tissue volume.
 19. The system of claim 13,wherein each of the plurality of sensors is configured to interrogatethe shared tissue volume at a distinct and different time period.
 20. Aphysiological monitoring system configured to determine a physiologicalparameter by harmonizing data between two or more different types ofnon-invasive physiological sensors interrogating a shared tissue volume,the physiological monitoring system comprising: a plethysmography sensorconfigured to generate a first signal indicative of first physiologicaldata associated with a shared tissue volume; a second non-invasivesensing device configured to generate a second signal indicative ofsecond physiological data associated with the shared tissue volume,wherein the second non-invasive sensing device comprises a Raman sensor;and one or more processors in communication with the plethysmographysensor and the second non-invasive sensing device, the one or moreprocessors configured to: receive the first signal and the secondsignal, determine a physiological parameter based at least in part onthe first signal and the second signal, and output a visual or audibleindication of the physiological parameter.
 21. The system of claim 20,wherein the one or more processors are further configured to calibratethe Raman sensor based at least in part on the first signal, wherein tocalibrate the Raman sensor, the one or more processors are configured toselect at least one of a focal depth, a focal length, a wavelength, or arefractive index associated with the Raman sensor.
 22. The system ofclaim 20, wherein the one or more processors are further configured todetermine an isolated Raman signal by reducing or removing at least oneof an effect of fluorescence or an effect of absorption from the secondsignal.